The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory...

In this paper we develop an analytically solvable and structurally estimable economic geography model and apply it to predict migration flows for the period following the CEE’s integration with the EU. The main innovation of our approach is that it endogenises both explanatory variables and...

In this paper, we consider semiparametric model averaging of the nonlinear dynamic time series system where the number of exogenous regressors is ultra large and the number of autoregressors is moderately large. In order to accurately forecast the response variable, we propose two semiparametric...

We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.

We consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish...

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run...

The forecast performance of the empirical ESTAR model of Taylor, Peel and Sarno (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample eval-uation period of nearly 12 years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22...

The Beveridge-Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The paper in-troduces the multistep Beveridge-Nelson decomposition, which arises when the forecast function is obtained...

We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and...

Few authors have studied, either asymptotically or in finite samples, the size and power of seasonal unit root tests when the data generating process [DGP] is a non-stationary alternative aside from the seasonal random walk. In this respect, Ghysels, lee and Noh (1994) conducted a simulation...